Claude Haiku 4.5 vs Gemini 2.5 Flash for Faithfulness

Faithfulness demands an AI stick closely to the provided source material, avoid inventing unsupported facts, and reliably map inputs into accurate outputs. Capabilities that matter most are: accurate retrieval over long contexts, reliable tool calling for source lookups, structured-output compliance to avoid format-driven distortions, and safety calibration to refuse illegitimate prompts without inventing content. In our testing (the faithfulness task), Claude Haiku 4.5 scored 5 while Gemini 2.5 Flash scored 4; Haiku ranks 1 of 52, Gemini ranks 33 of 52. Supporting proxy signals from our suite: both models score 5 on tool_calling and 5 on long_context, and both score 4 on structured_output — these explain why both handle source retrieval and formatting well. The tradeoff to note: Haiku's safety_calibration is 2 vs Gemini's 4, which affects how strictly the model refuses harmful or disallowed requests (see benchmark descriptions). Cost and modality are also relevant: Haiku's output cost is $5 per mTok vs Gemini's $2.5 per mTok, and Gemini accepts a wider set of input modalities (files/audio/video), which can improve end-to-end faithfulness when sources are non-text.

1. Legal contract extraction: In our tests Haiku's 5 vs Gemini's 4 means Claude Haiku 4.5 is more likely to reproduce contract clauses verbatim and avoid inserting unsupported terms — useful when exact fidelity to source wording matters. 2) Large-document summarization (30K+ tokens): Both models score 5 on long_context and 5 on tool_calling, so either can retrieve facts across long inputs; Haiku's higher faithfulness score still gives it a measurable edge in preserving factual details. 3) Multimodal source verification: Gemini 2.5 Flash supports files/audio/video inputs and has a stronger safety_calibration (4 vs Haiku's 2) and lower output cost ($2.5 vs $5 per mTok), so for pipelines that ingest transcripts, images plus audio, or need stricter refusal behavior, Gemini is the pragmatic choice despite scoring 4 on faithfulness. 4) High-volume, cost-sensitive pipelines that require reasonable fidelity: Gemini's lower output cost and 4/5 faithfulness make it the better cost-performance tradeoff. 5) Internal tool-driven citation workflows: both models score 5 on tool_calling, so either integrates well with tool-based source lookups; prefer Haiku when maximal literal fidelity is the priority, prefer Gemini when safety gating and multimodal inputs matter.

For Faithfulness, choose Claude Haiku 4.5 if you need the highest measured adherence to source text in our tests (5 vs 4) and can accept higher output cost ($5 per mTok) and a lower safety_calibration score. Choose Gemini 2.5 Flash if you need strong overall fidelity with better safety calibration (4 vs 2), broader multimodal input support, and lower output cost ($2.5 per mTok).